Effects of Temperature and Blinking on Contact Lens Dehydration of Contemporary Soft Lens Materials Using an In Vitro Blink Model
Bibliographic record
Abstract
Purpose: The purpose of this study was to evaluate the effects of temperature and blinking on contact lens (CL) dehydration using an in vitro blink model. Methods: Three silicone hydrogel (delefilcon A, senofilcon A, and comfilcon A) and two conventional hydrogel (etafilcon A and omafilcon A) CL materials were evaluated at 1 and 16 hours. The water content (WC) of the CLs was measured using a gravimetric method. Lenses were incubated on a blink model, internally heated to achieve a clinically relevant surface temperature of 35°C. An artificial tear solution (ATS) was delivered to the blink model at 4.5 µL/min with a blink rate of 6 blinks/min. A comparison set of lenses were incubated in a vial containing either 2 mL of ATS or phosphate-buffered saline (PBS) at 35°C. Results: Increasing temperature to 35°C resulted in a decrease in WC for all tested CLs over time (P ≤ 0.0052). For most CLs, there was no significant difference in WC over time between ATS or PBS in the vial (P > 0.05). With the vial system, WC decreased and plateaued over time. However, on the blink model, for most CLs, the WC significantly decreased after 1 hour but returned toward initial WC levels after 16 hours (P > 0.05). Conclusions: The reduction in WC of CLs on the eye is likely due to both an increase in temperature and dehydration from air exposure and blinking. Translational Relevance: This study showed that the novel, heated, in vitro blink model could be used to provide clinical insights into CL dehydration on the eye.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".